A user's satisfaction with a particular technology is based in large part on ease of use due to the user interface. The design of a user interface affects the amount of effort the user must expend to provide input for the system, to interpret the output of the system, and the amount of effort it takes to learn how to do this.
Many devices are controlled using physical knobs, sliders, buttons, or virtual controls such as touch-screens and soft buttons. However, these mechanisms often demand the user's visual attention and can be quite difficult, or even dangerous, to operate while driving.
A driver may require driving directions, or desire other information pertinent to his surroundings, but has limited options for accessing that information. Most onboard navigation systems are disabled while the vehicle is in motion and many states have enacted distracted driving laws that prohibit the use of handheld cell phones while driving. Basic safe driving wisdom dictates that the driver's hands be kept on the steering wheel and his eyes on the road. However, drivers still have a need for timely information and real-time driver assistance.
Interfaces between users and technology integrate a variety of means to allow the technology to receive input from a user. One direction pursued by interface designers has been in gesture recognition and similar technologies. Motions or gestures can be identified and analyzed to trigger action related to a variety of devices. The use of human motions or gestures to convey input to a device can provide a speedy, intuitive means to control the device, especially in instances when traditional device controls (e.g., keyboard, mouse) are not practical.
Despite clear advantages, fingertip recognition is often limited by the resolution of systems employing it. For example, while a conventional motion sensor can recognize the presence of a hand in frame (e.g., within the “view” of one or more sensors capable of collecting input relevant to tracking fingertip location), a precise determination of the location and orientation of a fingertip is not straight forward. Current systems lack the ability and precision to accurately identify and track fingertip location, severely limiting their use.
Existing gesture tracking techniques fail to accurately detect the precise three dimensional location, orientation and motion of a fingertip and suffer from problems related to susceptibility of sensor inputs to noise which causes jitter and inaccuracies at the output. A major drawback in conventional fingertip location tracking is the difficulty of accurate estimation of the pointing direction, caused by variations in recognition and tracking accuracy, and the unreliability of direction estimation.